Method, device, equipment and medium for detecting water storage space in coal mine goaf

Abstract

The application provides a coal mine goaf water storage space detection method, device, equipment and medium, relates to the technical field of coal mining and water resource protection, and the method comprises the following steps: obtaining environment data reflecting the difference of underground structure, determining an initial goaf based on the environment data; collecting three-dimensional point cloud data of the initial goaf, and constructing a cavity three-dimensional geometric model corresponding to the initial goaf based on the three-dimensional point cloud data; constructing a water storage space model of the overburden rock fracture zone according to the state parameters of the overburden rock located in the initial goaf; filling the initial goaf with materials based on the cavity three-dimensional geometric model and the water storage space model, and obtaining a target water storage space. Therefore, the application obtains the target water storage space by fusing multi-source data and performing space collaborative modeling and structure optimization filling, guarantees the boundary accuracy and structure reliability of the obtained target water storage space, and improves the long-term stability and water resource storage and utilization efficiency of the target water storage space.

Description

Technical Field

[0001] This application relates to the field of coal mining and water resource protection technology, and in particular to a method, apparatus, equipment and medium for detecting water storage space in coal mine goaf. Background Technology

[0002] Constructing underground reservoirs in goaf areas formed after coal mining is an important way to protect and utilize mine water. The core of this approach lies in accurately understanding the morphology, boundaries, and stability of the water storage space in the goaf. However, traditional detection technologies such as 3D laser scanning, drilling, and electrical resistivity tomography (EDT) all have significant limitations: 3D laser scanning can only acquire data on visible cavities and cannot penetrate the rock mass; drilling only provides point or line measurements and cannot achieve continuous spatial characterization; EDT has insufficient resolution and is easily affected by interference. These single technologies cannot comprehensively and accurately characterize the complex structure of goaf areas, resulting in low accuracy in subsequent 3D modeling, insufficient utilization of water storage space, and difficulty in assessing and ensuring its long-term stability.

[0003] In addition, existing modeling methods mostly rely on a single data source, do not fully consider the impact of overlying fissure development on water storage performance, and lack an effective monitoring and model updating mechanism for dynamic changes in water storage space, resulting in insufficient model accuracy and low utilization of water storage space. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for detecting water storage space in coal mine goaf areas, aiming to solve the problems of inaccurate goaf boundary identification due to reliance on a single detection method, and low water resource utilization rate caused by the lack of integration of overburden fracture characteristics and dynamic update mechanism in water storage space models. The technical solution is as follows: Firstly, a method for detecting water storage space in coal mine goaf is provided, including: Acquire environmental data reflecting differences in underground structures, and determine the initial goaf area based on the environmental data; Collect three-dimensional point cloud data of the initial goaf area, and construct a three-dimensional geometric model of the cavity corresponding to the initial goaf area based on the three-dimensional point cloud data; Based on the state parameters of the overlying strata in the initial goaf area, a water storage space model of the overlying strata fracture zone is constructed. Based on the three-dimensional geometric model of the cavity and the water storage space model, the initial goaf area is filled with materials to obtain the target water storage space.

[0005] In one possible implementation, the environmental data includes seismic reflection wave data and subsurface resistivity distribution data; The acquisition of environmental data reflecting differences in underground structures, and the determination of the initial goaf based on the environmental data, includes: A horizontal slice sequence is generated based on the earthquake reflection wave data; Analyze the reflected wave characteristics of each slice in the horizontal slice sequence to identify the color abrupt change regions in the slice; By combining the color abrupt change regions in all slices of the horizontal slice sequence, the boundary of the goaf is obtained; Based on the underground resistivity distribution data, resistivity anomaly areas are identified, and the resistivity anomaly areas are compared and verified with the boundary of the goaf. The boundary of the goaf is then corrected based on the verification results. The area defined by the modified goaf boundary is taken as the initial goaf.

[0006] In one possible implementation, three-dimensional point cloud data of the initial goaf is collected, and a three-dimensional geometric model of the cavity corresponding to the initial goaf is constructed based on the three-dimensional point cloud data, including: Three-dimensional point cloud data of the initial goaf area were acquired using laser scanning. The location and volume of the cavity in the initial goaf area are determined using the three-dimensional point cloud data. A three-dimensional geometric model of the cavity is constructed based on the cavity location and spatial volume of the initial goaf.

[0007] In one possible implementation, the method further includes: When the initial goaf area meets the accessibility conditions, local detailed point cloud data of the cavity is obtained through internal moving detection. The cavity three-dimensional geometric model is corrected using the local detail point cloud data.

[0008] In one possible implementation, the state parameters include fracture length, fracture aperture, and fracture connectivity coefficient; The step of constructing a water storage space model for the overlying fracture zone based on the state parameters of the overlying strata located in the initial goaf includes: The fracture development index is calculated based on the fracture length. The fracture density is calculated based on the fracture development index. The permeability function is calculated based on the fracture length, the fracture aperture, and the fracture connectivity coefficient. The water storage function is calculated based on the fracture length, the fracture aperture, the fracture connectivity coefficient, and the fracture density. A water storage space model is constructed based on the permeability function and the water storage function.

[0009] In one possible implementation, based on the three-dimensional geometric model of the cavity and the water storage space model, the initial goaf is filled with material to obtain the target water storage space, including: Based on the three-dimensional geometric model of the cavity, the range of unstable cavities in the initial goaf area that requires structural support is identified. Based on the aforementioned overburden fracture zone water storage space model, overburden fracture development areas with water storage potential are identified; Based on the range of the unstable cavity and the development area of ​​the overlying rock fissures, the spatial distribution and material performance requirements of the filling body are planned. According to the plan, paste material is filled into the location to be filled in order to obtain the target water storage space.

[0010] In one possible implementation, after obtaining the target water storage space, the method further includes: Real-time acquisition of the structural and seepage states of the target water storage space; Based on the structural state and the seepage state, the three-dimensional geometric model of the cavity and the water storage space model are dynamically updated.

[0011] Secondly, a detection device for water storage space in coal mine goaf is provided, comprising: The data acquisition module is used to acquire environmental data reflecting differences in underground structures and to determine the initial goaf based on the environmental data. The data processing module is used to collect three-dimensional point cloud data of the initial goaf area and construct a three-dimensional geometric model of the cavity corresponding to the initial goaf area based on the three-dimensional point cloud data. The data construction module is used to construct a water storage space model of the overlying rock fracture zone based on the state parameters of the overlying rock located in the initial goaf area. The data generation module is used to fill the initial goaf with materials based on the cavity three-dimensional geometric model and the water storage space model to obtain the target water storage space.

[0012] Thirdly, an electronic device is provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the method for detecting water storage space in a coal mine goaf as described in any of the preceding claims.

[0013] Fourthly, a storage medium is provided, the storage medium storing a computer program, wherein the computer program is configured to execute, at runtime, the method for detecting water storage space in coal mine goaf as described in any of the preceding claims.

[0014] The technical solutions provided in this application can achieve the following technical effects.

[0015] (1) By employing multi-source data fusion technology, the boundary of the initial goaf is accurately identified through mutual verification and iterative correction of seismic reflection wave data and underground resistivity distribution data. Combined with the fine reconstruction of the cavity morphology inside the initial goaf using three-dimensional point cloud data, and the quantitative characterization of the water storage performance of the fracture zone by overburden state parameters, the limitations of traditional single detection methods are overcome. The integrated and accurate characterization of the water storage space is achieved in multiple dimensions such as boundary location, structural stability, and fracture water storage capacity. This ensures that the constructed target water storage space has both structural stability and reliability and optimized water storage capacity, providing a reliable data foundation for the safety design, capacity assessment, and sustainable operation of subsequent water storage projects.

[0016] (2) Based on the obtained target water storage space, distributed fiber optic sensors are deployed inside it to form a three-dimensional monitoring network covering the key structural parts and seepage paths in the target water storage space. This network can collect monitoring data such as structural deformation and seepage status of the target water storage space in real time, and dynamically update the three-dimensional geometric model of the cavity and the water storage space model based on the monitoring data. This realizes the closed-loop management of the target water storage space from static modeling to dynamic operation and maintenance, thereby continuously tracking the dynamic evolution of the target water storage space, and timely identifying risks and optimizing control strategies, thereby significantly improving the long-term stability, safety and comprehensive utilization efficiency of the target water storage space. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. In the drawings: Figure 1 This is a flowchart of a method for detecting water storage space in a coal mine goaf according to an embodiment of this application; Figure 2 This is a flowchart illustrating the acquisition and processing of seismic reflection wave data in the embodiments of the method of this application; Figure 3 This is a flowchart illustrating the acquisition and processing of underground resistivity distribution data in the embodiments of the method of this application; Figure 4 This is an example diagram illustrating the use of internal motion detection to collect local details of a goaf in the embodiments of the method of this application; Figure 5 This is a flowchart of the detection method with a dynamic update mechanism in the embodiments of the method of this application; Figure 6 This is a block diagram of a detection device for water storage space in a coal mine goaf, according to an embodiment of this application. Figure 7 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0020] This application provides a method for detecting water storage space in coal mine goaf, such as... Figure 1 As shown, the method mainly includes steps S101 to S104.

[0021] S101: Obtain environmental data reflecting differences in underground structures, and determine the initial goaf based on the environmental data.

[0022] First, the coal mine goaf to be explored needs to be identified. A coal mine goaf refers to the underground voids and surrounding loosened rock strata formed after coal mining. Its boundaries are often blurred, its shape complex, and it is frequently accompanied by fracture development and localized collapse. To accurately identify and model the water-bearing space, environmental data reflecting the differences in physical properties between the coal mine goaf and the surrounding rock strata needs to be collected. Through comprehensive analysis and processing of this environmental data, the spatial extent of the coal mine goaf can be preliminarily delineated, thus obtaining the initial goaf area.

[0023] The aforementioned environmental data includes seismic reflection wave data and underground resistivity distribution data.

[0024] like Figure 2As shown, the process of acquiring seismic reflection wave data is as follows: Excitation points and a geophone array are deployed on the ground. Seismic waves are artificially generated at the excitation points. The seismic waves propagate downwards and are reflected when they encounter surfaces with impedance differences, such as the boundary of a coal mine goaf or a lithological interface. The geophone array receives the reflected wave signals, and all received reflected wave signals are combined as seismic reflection wave data. Based on the obtained seismic reflection wave data, reflected wave signals from the same interface at the same time are extracted and combined to generate a horizontal slice. By superimposing slices at different depths, a sequence of horizontal slices covering the coal mine goaf is formed. By analyzing the amplitude and phase characteristics of the reflected waves in each slice of the horizontal slice sequence, regions of color abrupt changes caused by the existence of the coal mine goaf are identified, typically manifested as interruptions in the phase axis, abrupt amplitude changes, or waveform disorder. Each slice in the horizontal slice sequence is traversed, and the color anomaly regions in each slice are interpolated and fitted to surfaces in three-dimensional space to form a preliminary goaf boundary. During the fitting process, if the goaf boundary exhibits significant discontinuities, self-intersections, or serious contradictions with known geological structures, the generated goaf boundary is considered inaccurate. In this case, it is necessary to readjust the seismic wave excitation parameters or detector layout and re-acquire and process the seismic reflection wave data until the goaf boundary is geometrically and geologically reasonable. Otherwise, the goaf boundary is smoothed and topologically verified during the fitting process to output a preliminary goaf boundary.

[0025] like Figure 3 As shown, the process of collecting underground resistivity distribution data is as follows: Transmitting and receiving devices are deployed on the ground. A specific frequency alternating current is injected into the coal mine goaf through the transmitting device. This alternating current propagates in the underground medium and excites an electromagnetic field. Different media respond differently to the electromagnetic field due to differences in electrical properties. The receiving device simultaneously collects the electromagnetic field signals generated by different underground media. Based on the collected electromagnetic field signals, the resistivity distribution data of the underground medium is calculated using an inversion algorithm. Based on the obtained resistivity distribution data, low-resistivity or high-resistivity anomaly areas caused by water filling, fissure development, or the presence of cavities in the goaf are identified. These anomaly areas are spatially superimposed and compared with the goaf boundary delineated by the seismic method. By comparing the spatial overlap, morphological consistency, and electrical-wave impedance correspondence between the two, the goaf boundary is corrected by calibration, contraction, or expansion. The spatial area defined by the corrected goaf boundary is used as the final determined initial goaf area. If the verification fails during the calibration process, that is, if there is a significant contradiction between the resistivity anomaly region and the goaf boundary delineated by the seismic method in terms of spatial overlap, morphological consistency, or electrical-wave impedance correspondence, the current resistivity anomaly region is considered unreliable. It is necessary to readjust the transmission frequency and the layout of the receiving device, and to re-acquire and process the electromagnetic field signal until the comparison verification between the resistivity anomaly region and the goaf boundary delineated by the seismic method is successful.

[0026] Therefore, the initial goaf area is determined by integrating multi-source data such as seismic reflection wave data and underground resistivity distribution data, and through multiple rounds of iterative processes of spatial comparison, geometric fitting and physical property verification, thus ensuring the accuracy of the obtained initial goaf area.

[0027] S102: Collect three-dimensional point cloud data of the initial goaf area, and construct a three-dimensional geometric model of the cavity corresponding to the initial goaf area based on the three-dimensional point cloud data.

[0028] Based on the initial goaf area, it is necessary to further collect detailed three-dimensional spatial data of its internal cavity to construct a three-dimensional geometric model of the cavity that accurately reflects its actual shape and volume. This step involves acquiring three-dimensional point cloud data of the initial goaf area using laser scanning and constructing a three-dimensional geometric model of the cavity based on this data. The specific process includes the following steps: First, a 3D laser scanning device is deployed into the initial goaf through boreholes. Using laser ranging principles, a high-density scan of the cavity boundary surface is performed to acquire 3D point cloud data reflecting the internal geometry of the cavity. This 3D point cloud data records the 3D coordinates of a large number of points on the cavity boundary surface, constituting a discrete spatial representation of the cavity. Next, the acquired 3D point cloud data undergoes preprocessing, including coordinate system unification, noise removal, and data registration, to ensure data consistency and integrity. Based on the processed 3D point cloud data, a 3D reconstruction algorithm (such as triangulation or surface fitting) is used to construct a continuous geometric surface model of the cavity, thereby determining the cavity's spatial location, morphological characteristics, and total volume, forming a 3D geometric model of the cavity corresponding to the initial goaf.

[0029] Furthermore, for initial goaf areas (such as room-and-pillar goaf areas) where personnel or mobile equipment can enter, supplementary detection can be implemented to improve the detail and local accuracy of the cavity's three-dimensional geometric model. Specifically, this involves deploying laser scanning equipment, Simultaneous Localization and Mapping (SLAM) equipment, and a GNSS inertial navigation system on a UAV, and then controlling the UAV to perform continuous scanning tasks according to a pre-planned flight path to obtain local detailed point cloud data of the cavity within the initial goaf area. This local detailed point cloud data is then fused, registered, and integrated with the overall three-dimensional point cloud data obtained from the aforementioned borehole scanning. The local detailed point cloud data is used to refine and optimize the local structures (such as coal pillar morphology, roof undulations, and corner areas) in the cavity's three-dimensional geometric model formed by the borehole scanning, ultimately resulting in a high-precision three-dimensional geometric model of the cavity that accurately represents its true geometric morphology. Figure 4 As shown, after performing a detailed scan of the room-and-pillar goaf using an internal moving detection method, local details of the room-and-pillar goaf were obtained, such as... Figure 4More local details of the goaf within the rectangular frame, and local structures shown in the directions indicated by arrows.

[0030] Through the above-mentioned process of collecting and fusing multi-source, multi-scale point cloud data, we not only achieved effective detection of cavities in inaccessible areas, but also further improved the local accuracy of the accessible area model through internal movement detection, thus providing a reliable three-dimensional geometric basis for cavity stability analysis, water storage capacity assessment and subsequent engineering modifications.

[0031] S103: Construct a water storage space model for the overlying rock fracture zone based on the state parameters of the overlying rock located in the initial goaf area.

[0032] Overburden refers to the rock strata located above a goaf that have developed fractures due to mining disturbances. The internal fracture network constitutes an important water storage and conduction space. This embodiment constructs a digital model that quantitatively characterizes the water storage capacity and seepage characteristics of the overburden through its state parameters, thereby achieving a refined assessment of the water storage space in the fracture zone.

[0033] The aforementioned state parameters include, but are not limited to, fracture length, fracture aperture, and fracture connectivity coefficient. Fracture length and fracture aperture are obtained through core logging of borehole core samples and combined with 3D optical or laser scanning within the borehole. The fracture connectivity coefficient, on the other hand, is a parameter used to characterize the degree of hydraulic connectivity between fractures, calculated based on fracture statistical data (such as density, orientation, and cross-cutting relationships) through topological analysis or empirical relationships.

[0034] The following describes the process of generating a water storage space model of the overlying fracture zone based on the state parameters of the overlying strata.

[0035] 1) Calculation of fracture development index: Based on the segmented core observation results, fractures with a length greater than the preset length are counted and marked as qualified fractures. The total length of all qualified fractures is then taken as the effective length, and the fracture development index (RQD) is calculated using the following formula: ; in, L 合格,i Indicates the first i The length of a qualified crack segment N This indicates the total number of qualified fracture segments. L 总 Indicates the effective length.

[0036] In this embodiment, the RQD value is used to reflect the degree of fracture development in the overlying strata. Specifically, the lower the RQD value, the more developed the fractures and the worse the integrity of the overlying strata.

[0037] 2) Determine fracture density: Based on historical test data or regional statistical data, establish an empirical relationship model between RQD value and fracture density. The relationship model is expressed by the following calculation formula: ; in, D Indicates the fracture density. a , b , c All represent the fitting coefficients, which are obtained from measured data through regression analysis.

[0038] As shown in the above formula, after obtaining the RQD value, substituting the RQD value into the above relational model allows us to calculate the fracture density corresponding to the RQD value. D .

[0039] 3) Constructing the permeability function: Calculating the average fracture length based on fracture length statistics. Then, based on the average length of the crack , fracture aperture b and fracture connectivity coefficient C By combining the cubic law or the theory of equivalent continuous media, the equivalent permeability of the fracture network is calculated. k That is, to establish the osmotic function: ; in, Represents gravitational acceleration. The usual value is 9.8m / m 2 , μ Indicates the dynamic viscosity of water. L 0 represents the reference length, which is usually taken as 1m or determined according to the characteristics of the rock mass structure. m , n These are correction coefficients calibrated based on experiments or numerical simulations.

[0040] 4) Constructing the water storage function: Combining the average fracture length , fracture aperture b , fracture connectivity coefficient C and fracture density D Using the theoretical formula for fracture water storage capacity, the water storage coefficient, which characterizes the water storage capacity per unit volume of overburden, is calculated. S That is, to establish the water storage function: .

[0041] 5) Construct a water storage space model based on the permeability function and storage function. Use the calculated permeability... k With water storage coefficient SAs attribute parameters, they are coupled with the three-dimensional spatial framework of the overlying strata to construct a water-retaining space model of the overlying fracture zone that can simultaneously characterize water storage capacity and seepage characteristics. This model can be visualized and numerically analyzed in three-dimensional modeling software (such as 3DMINE, FLAC3D, etc.).

[0042] In other words, state parameters of the overlying strata were obtained through core scanning observations, and attribute parameters for each dimension were calculated using theoretical formulas and empirical relationships. Based on these attribute parameters, a digital model of the water-bearing space in the overlying fracture zone was finally constructed, integrating geometric and hydraulic properties. This model represents a breakthrough in the qualitative description and quantitative characterization of the water-bearing structure in fracture zones, providing a data foundation for the comprehensive evaluation and utilization of subsequent water-bearing spaces.

[0043] S104: Based on the three-dimensional geometric model of the cavity and the water storage space model, the initial goaf area is filled with materials to obtain the target water storage space.

[0044] First, based on the three-dimensional geometric model of the cavity, the stability of the cavities within the initial goaf is assessed. By analyzing the cavity's geometry (such as span-to-height ratio and roof curvature), spatial distribution, and positional relationship with key geological structures, unstable cavity areas requiring structural reinforcement due to their large span, weak roof, or location in stress concentration zones are identified. These areas are the core regions where structural support should be prioritized in subsequent filling works.

[0045] Simultaneously, based on the water storage space model of the overlying fracture zone, the water storage potential of the overlying fracture zone was quantitatively analyzed. By extracting the permeability and water storage coefficient distribution from the model, and combining it with fracture density and fracture connectivity, advantageous fracture development areas with good permeability, strong water storage capacity, and high hydraulic continuity were identified. These areas are key spaces that need to be protected or utilized specifically during the filling process to maximize the preservation of natural water storage capacity.

[0046] Then, based on the above identification results, the filling project is planned. Specifically, based on the spatial location and shape of the unstable cavity range, the spatial distribution, shape, and mechanical strength requirements of the filling material as a structural support are determined; based on the distribution characteristics of the dominant fracture development area, the permeability control requirements of the filling material and the reservation of water storage channels for non-filled or low-strength filling are planned. Finally, a spatial layout and material performance design scheme for the filling material that takes into account both the dual objectives of "support and reinforcement" and "water storage protection" is formed.

[0047] Finally, precise filling construction is carried out according to the planned scheme. Pumping is typically used to fill the planned filling locations with paste material. During the filling process, vibration and pressure control are employed to ensure the compaction of the filling material. The filling material formed using this method effectively supports the overburden, controls surrounding rock deformation and surface subsidence, and improves the long-term stability of the overall structure of the initial goaf. Furthermore, its spatial layout and material properties ensure the efficient utilization of the water storage space within the cavities of the initial goaf, ultimately constructing a target water storage space with high stability, large water storage capacity, and strong controllability. This target water storage space lays the engineering foundation for the safe construction and sustainable operation of coal mine goafs.

[0048] In one possible implementation, such as Figure 5 As shown, after obtaining the target water storage space, in order to achieve long-term dynamic monitoring of its operating status and continuous optimization of the model, distributed fiber optic sensors are pre-embedded in the filling body to form a three-dimensional monitoring network covering key structural parts and seepage paths in the target water storage space. This monitoring network can collect deformation and strain data of the filling body inside the target water storage space, as well as monitoring data such as fracture water pressure and seepage parameters in real time and synchronously.

[0049] Based on real-time acquired monitoring data, the results are compared in real time with numerical simulation results obtained by coupled calculation based on the cavity three-dimensional geometric model and the water storage space model. This allows for the calculation of the deviation between the actual water storage space and the target water storage space predicted by the model. Then, based on the deviation value, the geometric boundaries, material property parameters (such as permeability and water storage coefficient) and boundary conditions of the cavity three-dimensional geometric model and the water storage space model are quantitatively corrected and dynamically updated. This enables continuous tracking and accurate reflection of the state evolution of the target water storage space, providing real-time and accurate digital basis for the safety assessment, risk warning and control decision-making of the target water storage space.

[0050] In summary, the implementation principle of the method for detecting water storage space in a coal mine goaf according to the embodiments of this application is as follows: First, by fusing multi-source data such as seismic reflection wave data and underground resistivity distribution data, spatial comparison and iterative verification are performed to accurately delineate the initial goaf. Second, laser scanning technology is used to acquire three-dimensional point cloud data of the cavity boundary within the initial goaf, and through processing and three-dimensional reconstruction of the three-dimensional point cloud data, a three-dimensional geometric model of the cavity corresponding to the initial goaf is constructed. Then, the state parameters of the overlying strata located in the initial goaf are collected, and through the calculation of fracture development index, fracture density estimation, and hydraulic function construction, a water storage space model of the overlying fracture zone that quantitatively characterizes the storage-permeability is formed. Finally, based on the three-dimensional geometric model of the cavity and the water storage space model, artificial material filling is planned and implemented collaboratively to construct a high-precision target water storage space with stable structure and preserved and strengthened water storage space, thereby significantly improving the safety and utilization rate of the target water storage space. In addition, a monitoring network deployed within the filling body is used to collect deformation and seepage data in real time, which drives the model to be dynamically updated and corrected, enabling continuous tracking and digital control of the target water storage space and ensuring its long-term safe and efficient operation.

[0051] It should be noted that the sequence numbers of the steps in the above embodiments do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. In practical applications, all the above possible implementation methods can be arbitrarily combined in a combined manner to form possible embodiments of this application, which will not be described in detail here.

[0052] Based on the method for detecting water storage space in a coal mine goaf provided in the above embodiments, and based on the same inventive concept, this application also provides a device for detecting water storage space in a coal mine goaf.

[0053] Figure 6 This is a structural diagram of a detection device for water storage space in a coal mine goaf, provided in an embodiment of this application. Figure 6 As shown, the device may specifically include a data acquisition module, a data processing module, a data construction module, and a data generation module. The modules are described below: The data acquisition module is used to acquire environmental data reflecting differences in underground structures and to determine the initial goaf based on the environmental data. The data processing module is used to collect three-dimensional point cloud data of the initial goaf area and construct a three-dimensional geometric model of the cavity corresponding to the initial goaf area based on the three-dimensional point cloud data. The data construction module is used to construct a water storage space model of the overlying rock fracture zone based on the state parameters of the overlying rock in the initial goaf area. The data generation module is used to fill the initial goaf with materials based on the three-dimensional geometric model of the cavity and the water storage space model to obtain the target water storage space.

[0054] In one possible implementation, the detection device further includes a data update module, which is used to collect the structural state and seepage state of the target water storage space in real time after obtaining the target water storage space; and to dynamically update the three-dimensional geometric model of the cavity and the water storage space model based on the structural state and seepage state.

[0055] This embodiment provides a detection device for water storage space in a coal mine goaf, used to execute the detection method for water storage space in a coal mine goaf provided in the above embodiment. Its implementation method and principle are the same. For details of the implementation method of each module, please refer to the relevant description of the above method embodiment, which will not be repeated here.

[0056] Based on the same inventive concept, this application also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a method for detecting water storage space in a coal mine goaf according to any of the above embodiments.

[0057] In an exemplary embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The illustrated electronic device 700 includes a processor 701 and a memory 703. The processor 701 and the memory 703 are connected, for example, via a bus 702. Optionally, the electronic device 700 may also include a transceiver 704. It should be noted that in practical applications, the transceiver 704 is not limited to one type, and the structure of this electronic device 700 does not constitute a limitation on the embodiments of this application.

[0058] Processor 701 may be a CPU (Central Processing Unit), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 701 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0059] Bus 702 may include a pathway for transmitting information between the aforementioned components. Bus 702 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 702 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0060] The memory 703 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0061] The memory 703 stores computer program code that executes the scheme of this application, and its execution is controlled by the processor 701. The processor 701 executes the computer program code stored in the memory 703 to implement the content shown in the foregoing method embodiments.

[0062] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0063] Based on the same inventive concept, this application also provides a storage medium storing a computer program, wherein the computer program is configured to execute, at runtime, a method for detecting water storage space in a coal mine goaf according to any of the above embodiments.

[0064] Those skilled in the art will clearly understand that the specific working process of the systems, devices, and modules described above can be referred to the corresponding process in the foregoing method embodiments. For the sake of brevity, it will not be repeated here.

[0065] Those skilled in the art will understand that the technical solution of this application, or all or part of it, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several program instructions to cause an electronic device (e.g., a personal computer, server, or network device) to execute all or part of the steps of the methods described in the embodiments of this application when running the program instructions. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0066] Alternatively, all or part of the steps of the foregoing method embodiments can be implemented by hardware (such as electronic devices like personal computers, servers, or network devices) associated with program instructions. The program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of this application.

[0067] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that within the spirit and principles of this application, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the corresponding technical solutions to leave the protection scope of this application.

Claims

1. A method for detecting water storage space in a coal mine goaf, characterized in that, include: Acquire environmental data reflecting differences in underground structures, and determine the initial goaf area based on the environmental data; Collect three-dimensional point cloud data of the initial goaf area, and construct a three-dimensional geometric model of the cavity corresponding to the initial goaf area based on the three-dimensional point cloud data; Based on the state parameters of the overlying strata in the initial goaf area, a water storage space model of the overlying strata fracture zone is constructed. Based on the three-dimensional geometric model of the cavity and the water storage space model, the initial goaf area is filled with materials to obtain the target water storage space.

2. The detection method according to claim 1, characterized in that, The environmental data includes seismic reflection wave data and underground resistivity distribution data; The acquisition of environmental data reflecting differences in underground structures, and the determination of the initial goaf based on the environmental data, includes: A horizontal slice sequence is generated based on the earthquake reflection wave data; Analyze the reflected wave characteristics of each slice in the horizontal slice sequence to identify the color abrupt change regions in the slice; By combining the color abrupt change regions in all slices of the horizontal slice sequence, the boundary of the goaf is obtained; Based on the underground resistivity distribution data, resistivity anomaly areas are identified, and the resistivity anomaly areas are compared and verified with the boundary of the goaf. The boundary of the goaf is then corrected based on the verification results. The area defined by the modified goaf boundary is taken as the initial goaf.

3. The detection method according to claim 1, characterized in that, Collect three-dimensional point cloud data of the initial goaf area, and construct a three-dimensional geometric model of the cavity corresponding to the initial goaf area based on the three-dimensional point cloud data, including: Three-dimensional point cloud data of the initial goaf area were acquired using laser scanning. The location and volume of the cavity in the initial goaf area are determined using the three-dimensional point cloud data. A three-dimensional geometric model of the cavity is constructed based on the cavity location and spatial volume of the initial goaf.

4. The detection method according to claim 3, characterized in that, The method further includes: When the initial goaf area meets the accessibility conditions, local detailed point cloud data of the cavity is obtained through internal moving detection. The cavity three-dimensional geometric model is corrected using the local detail point cloud data.

5. The detection method according to claim 1, characterized in that, The state parameters include fracture length, fracture aperture, and fracture connectivity coefficient; The step of constructing a water storage space model for the overlying fracture zone based on the state parameters of the overlying strata located in the initial goaf includes: The fracture development index is calculated based on the fracture length. The fracture density is calculated based on the fracture development index. The permeability function is calculated based on the fracture length, the fracture aperture, and the fracture connectivity coefficient. The water storage function is calculated based on the fracture length, the fracture aperture, the fracture connectivity coefficient, and the fracture density. A water storage space model is constructed based on the permeability function and the water storage function.

6. The detection method according to claim 5, characterized in that, Based on the three-dimensional geometric model of the cavity and the water storage space model, the initial goaf area is filled with materials to obtain the target water storage space, including: Based on the three-dimensional geometric model of the cavity, the range of unstable cavities in the initial goaf area that requires structural support is identified. Based on the aforementioned overburden fracture zone water storage space model, overburden fracture development areas with water storage potential are identified; Based on the range of the unstable cavity and the development area of ​​the overlying rock fissures, the spatial distribution and material performance requirements of the filling body are planned. According to the plan, paste material is filled into the location to be filled in order to obtain the target water storage space.

7. The detection method according to claim 1, characterized in that, After obtaining the target water storage space, the method further includes: Real-time acquisition of the structural and seepage states of the target water storage space; Based on the structural state and the seepage state, the three-dimensional geometric model of the cavity and the water storage space model are dynamically updated.

8. A device for detecting water storage space in a coal mine goaf, characterized in that, include: The data acquisition module is used to acquire environmental data reflecting differences in underground structures and to determine the initial goaf based on the environmental data. The data processing module is used to collect three-dimensional point cloud data of the initial goaf area and construct a three-dimensional geometric model of the cavity corresponding to the initial goaf area based on the three-dimensional point cloud data. The data construction module is used to construct a water storage space model of the overlying rock fracture zone based on the state parameters of the overlying rock located in the initial goaf area. The data generation module is used to fill the initial goaf with materials based on the cavity three-dimensional geometric model and the water storage space model to obtain the target water storage space.

9. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the method for detecting water storage space in a coal mine goaf according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute, when running, the method for detecting water storage space in the goaf of a coal mine as described in any one of claims 1 to 7.

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